ALCOA+ In Data Integrity: A Comprehensive Guide 101

An organization’s success or failure is always determined by how well it is using the data it has about its processes and customers to derive business insights. The analytics revolution in recent years brought even more significance to data. In this blog, you will understand how ALCOA+ principles contribute to good Data Integrity which in turn results in good data analytics. Analytics can only work its magic if the underlying data is consistent and accurate.

Data Integrity is the term that denotes the quality of the data concerning accuracy and consistency. Over the years, many organizations have tried to define standards and frameworks to define the integrity of data. Among these standards, the one defined by the US Food And Drug Administration is called ALCOA+. Pharmaceutical research, manufacturing processes, testing, etc. use the ALCOA+ principles for good Data Integrity. This blog is about the importance of ALCOA+ in Data Integrity.

Understanding ALCOA+

ALCOA is an acronym coined by the office of the US Food And Drugs Administration in the 1990s. It is a set of guiding principles for ensuring Data Integrity and acts as the cornerstone of Good Documentation Processes (GDP). The principles apply to both electronic and paper-based data. ALCOA stands for Attributable, Legible, Contemporaneous, Original, and Accurate. 

In the 2010s, four more principles were added to ALCOA to reflect the current happenings and it was renamed to ALCOA+. Complete, Consistent, Enduring, and Available were added as part of this transition. As a whole, they serve as a base framework for handling data in Good Manufacturing Practices (GMP) and Good Documentation Practices (GDP).

Understanding the Importance of 9 Key ALCOA+ Principles

Now that you understand the purpose of ALCOA+ and where it is used, let’s discuss each of the 9 principles in ALCOA+ and how each principle contributes to the maintenance of Data Integrity.

• Attributable
• Legible
• Contemporaneous
• Original
• Accurate
• Complete
• Consistent
• Enduring
• Available

1. Attributable

Data must be attributable means that all collected data must-have information on who collected the data, who acted, and when the action was performed. This is extremely important in manufacturing processes and drug research where every data point must be accountable for and will be required in all further approval processes. If a data point is altered, that also must-have information on who performed it, along with the time of action. 

When it comes to Data Integrity, if the data is attributable, it contains more information and as a result, we can derive deeper and accurate insights from data. For example, if a cloth retailer wants to know which of its branches are performing great for the past 1 month then they need to have data about their sales along with the branch names and time stamps.

2. Legible

Legibility means the collected data must be precise and understandable. This is a reminder of the bygone era where most data collection was manual and paper-based. In the electronic age, legibility is already taken care of by the systems. But the concept of legibility is not only about reading the written information, but also about the context of information too. So in the case of electronic records, special care to record the audit trail and the context of data collected must be taken. 

3. Contemporaneous

This means that the data must be recorded at the same time the action is performed. Again, this is extremely important in pharmaceutical research and the manufacturing process. If you think about it, this is great to have as a feature for almost all of the analytics use cases too.

For example, consider an IoT use case where a sensor is collecting data. Contemporaneous collection means, data must be collected as and when the event happens and should have a timestamp associated with it. This also means the system must have a master clock based on which time is calculated.

4. Original

Data must be original. It means that the point or medium at which the data is recorded for the first time must be used for further processing. In other words, there should not be a secondary source of data. There must be protocols in place both in case of manual as well as electronic data collection to ensure this.

5. Accurate

The most basic requirement for any data point to be used in the analysis is that the data should be accurate. It should be complete and free from errors. Any changes or updates must be performed only per the Good Documentation Practices (GDP). In the case of manual data collection, this means having multiple individuals check the accuracy of data. In the case of electronic data collection, this means there must be redundant or duplicate systems in place to verify the accuracy. 

6. Complete

All data that is recorded should be complete. There should be audit trails to ensure that any changes to the data are captured with respect to the source of change as well as time. Nothing must be deleted or lost without reasons and an audit trail to capture it. 

7. Consistent

This means that all data points must have a date and time attached to them and it should be possible to create a chronology or sequence of events based on data. The captured sequence must match the expected sequence. 

8. Enduring

Enduring denotes the storage of data safely, long after the event has happened. This signifies the ability to store data in reliable places for a long duration- either as manual records or as a database. Data replication may help us to store our data in multiple places.

9. Available

Data must be available whenever needed. The key difference from the previous construct is that this one emphasizes the ability to retrieve data at any point in time and not only about storing data. This principle touches upon properly accounting data in the form of indexes or labels. 

Conclusion

Even though ALCOA+ principles were initially conceived for a pharmaceutical domain, they can make a difference in almost all the domains where data is used. ALOCA+ principles ensure that your data always has an audit trail to capture any addition, update, or deletion. In the current analytics-based decision-making scenario, following the decade-old principles of ALCOA+ can ensure that you always base your decisions on correct data whose integrity can be verified at any time. 

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